scholarly journals Tools and best practices for allelic expression analysis

2015 ◽  
Author(s):  
Stephane E Castel ◽  
Ami Levy-Moonshine ◽  
Pejman Mohammadi ◽  
Eric Banks ◽  
Tuuli Lappalainen
Keyword(s):  
Best Practices ◽  
Read Depth ◽  
Allelic Expression ◽  
Rna Seq ◽  
Sequencing Data ◽  
Technical Noise ◽  
Nonsense Mediated Decay ◽  
Biological Phenomena ◽  
Mapping Bias ◽  
Technical Bias

Allelic expression (AE) analysis has become an important tool for integrating genome and transcriptome data to characterize various biological phenomena such as cis-regulatory variation and nonsense-mediated decay. In this paper, we systematically analyze the properties of AE read count data and technical sources of error, such as low-quality or double-counted RNA-seq reads, genotyping errors, allelic mapping bias, and technical covariates due to sample preparation and sequencing, and variation in total read depth. We provide guidelines for correcting and filtering for such errors, and show that the resulting AE data has extremely low technical noise. Finally, we introduce novel software for high-throughput production of AE data from RNA-sequencing data, implemented in the GATK framework. These improved tools and best practices for AE analysis yield higher quality AE data by reducing technical bias. This provides a practical framework for wider adoption of AE analysis by the genomics community.

scholarly journals Making the most of RNA-seq: Pre-processing sequencing data with Opossum for reliable SNP variant detection

2017 ◽  
Vol 2 ◽  
pp. 6 ◽  
Author(s):  
Laura Oikkonen ◽  
Stefano Lise
Keyword(s):  
Best Practices ◽  
Transcriptome Sequencing ◽  
Substantial Reduction ◽  
Variant Calling ◽  
Cost Effective ◽  
Limited Time ◽  
Rna Seq ◽  
Sequencing Data ◽  
Variant Detection ◽  
Computational Resources

Identifying variants from RNA-seq (transcriptome sequencing) data is a cost-effective and versatile alternative to whole-genome sequencing. However, current variant callers do not generally behave well with RNA-seq data due to reads encompassing intronic regions. We have developed a software programme called Opossum to address this problem. Opossum pre-processes RNA-seq reads prior to variant calling, and although it has been designed to work specifically with Platypus, it can be used equally well with other variant callers such as GATK HaplotypeCaller. In this work, we show that using Opossum in conjunction with either Platypus or GATK HaplotypeCaller maintains precision and improves the sensitivity for SNP detection compared to the GATK Best Practices pipeline. In addition, using it in combination with Platypus offers a substantial reduction in run times compared to the GATK pipeline so it is ideal when there are only limited time or computational resources available.

scholarly journals Making the most of RNA-seq: Pre-processing sequencing data with Opossum for reliable SNP variant detection

2017 ◽  
Vol 2 ◽  
pp. 6 ◽  
Author(s):  
Laura Oikkonen ◽  
Stefano Lise
Keyword(s):  
Best Practices ◽  
Transcriptome Sequencing ◽  
Gene Expression Analysis ◽  
Substantial Reduction ◽  
Variant Calling ◽  
Rna Seq ◽  
Sequencing Data ◽  
Dna Variants ◽  
Variant Detection ◽  
Computational Resources

RNA-seq (transcriptome sequencing) is primarily considered a method of gene expression analysis but it can also be used to detect DNA variants in expressed regions of the genome. However, current variant callers do not generally behave well with RNA-seq data due to reads encompassing intronic regions. We have developed a software programme called Opossum to address this problem. Opossum pre-processes RNA-seq reads prior to variant calling, and although it has been designed to work specifically with Platypus, it can be used equally well with other variant callers such as GATK HaplotypeCaller. In this work, we show that using Opossum in conjunction with either Platypus or GATK HaplotypeCaller maintains precision and improves the sensitivity for SNP detection compared to the GATK Best Practices pipeline. In addition, using it in combination with Platypus offers a substantial reduction in run times compared to the GATK pipeline so it is ideal when there are only limited time or computational resources available.

scholarly journals Current RNA-seq methodology reporting limits reproducibility

2019 ◽  
Author(s):  
Joël Simoneau ◽  
Simon Dumontier ◽  
Ryan Gosselin ◽  
Michelle S Scott
Keyword(s):  
Ribonucleic Acid ◽  
Biological Sample ◽  
Rna Seq ◽  
Sequencing Data ◽  
Current Standard ◽  
Bioinformatics Pipeline ◽  
Technical Noise ◽  
Rna Molecules ◽  
Meaningful Role ◽  
Current Standard Practice

Abstract Ribonucleic acid sequencing (RNA-seq) identifies and quantifies RNA molecules from a biological sample. Transformation from raw sequencing data to meaningful gene or isoform counts requires an in silico bioinformatics pipeline. Such pipelines are modular in nature, built using selected software and biological references. Software is usually chosen and parameterized according to the sequencing protocol and biological question. However, while biological and technical noise is alleviated through replicates, biases due to the pipeline and choice of biological references are often overlooked. Here, we show that the current standard practice prevents reproducibility in RNA-seq studies by failing to specify required methodological information. Peer-reviewed articles are intended to apply currently accepted scientific and methodological standards. Inasmuch as the bias-less and optimal RNA-seq pipeline is not perfectly defined, methodological information holds a meaningful role in defining the results. This work illustrates the need for a standardized and explicit display of methodological information in RNA-seq experiments.

scholarly journals Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Zeeshan Ahmed ◽  
Eduard Gibert Renart ◽  
Saman Zeeshan ◽  
XinQi Dong
Keyword(s):  
Data Analysis ◽  
Patient Care ◽  
Expression Analysis ◽  
High Throughput ◽  
Gene Annotation ◽  
Next Generation Sequencing Data ◽  
Rna Seq ◽  
Sequencing Data ◽  
Complex Disorders ◽  
Transcriptomics Data

Abstract Background Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists. Results In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases. Conclusions We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

scholarly journals Best practices on the differential expression analysis of multi-species RNA-seq

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Matthew Chung ◽  
Vincent M. Bruno ◽  
David A. Rasko ◽  
Christina A. Cuomo ◽  
José F. Muñoz ◽  
...  
Keyword(s):  
Best Practices ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Single Species ◽  
Rna Seq ◽  
Species Analysis ◽  
Differential Gene ◽  
Multiple Species ◽  
Downstream Analysis

AbstractAdvances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

scholarly journals Replicate sequencing libraries are important for quantification of allelic imbalance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Asia Mendelevich ◽  
Svetlana Vinogradova ◽  
Saumya Gupta ◽  
Andrey A. Mironov ◽  
Shamil R. Sunyaev ◽  
...  
Keyword(s):  
Allelic Imbalance ◽  
False Positive Rate ◽  
Error Rates ◽  
Differential Analysis ◽  
Rna Seq ◽  
Specific Expression ◽  
Technical Noise ◽  
Specific Analysis ◽  
Positive Rate ◽  
Allele Specific

AbstractA sensitive approach to quantitative analysis of transcriptional regulation in diploid organisms is analysis of allelic imbalance (AI) in RNA sequencing (RNA-seq) data. A near-universal practice in such studies is to prepare and sequence only one library per RNA sample. We present theoretical and experimental evidence that data from a single RNA-seq library is insufficient for reliable quantification of the contribution of technical noise to the observed AI signal; consequently, reliance on one-replicate experimental design can lead to unaccounted-for variation in error rates in allele-specific analysis. We develop a computational approach, Qllelic, that accurately accounts for technical noise by making use of replicate RNA-seq libraries. Testing on new and existing datasets shows that application of Qllelic greatly decreases false positive rate in allele-specific analysis while conserving appropriate signal, and thus greatly improves reproducibility of AI estimates. We explore sources of technical overdispersion in observed AI signal and conclude by discussing design of RNA-seq studies addressing two biologically important questions: quantification of transcriptome-wide AI in one sample, and differential analysis of allele-specific expression between samples.

scholarly journals Transcriptome Analysis of Responses to Dengue Virus 2 Infection in Aedes albopictus (Skuse) C6/36 Cells

Viruses ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 343
Author(s):  
Manjin Li ◽  
Dan Xing ◽  
Duo Su ◽  
Di Wang ◽  
Heting Gao ◽  
...  
Keyword(s):  
Dengue Virus ◽  
Aedes Albopictus ◽  
Bioinformatics Analysis ◽  
Interaction Mechanism ◽  
Transcriptional Analysis ◽  
Functional Verification ◽  
Mosquito Vector ◽  
Rna Seq ◽  
Sequencing Data ◽  
Qrt Pcr

Dengue virus (DENV), a member of the Flavivirus genus of the Flaviviridae family, can cause dengue fever (DF) and more serious diseases and thus imposes a heavy burden worldwide. As the main vector of DENV, mosquitoes are a serious hazard. After infection, they induce a complex host–pathogen interaction mechanism. Our goal is to further study the interaction mechanism of viruses in homologous, sensitive, and repeatable C6/36 cell vectors. Transcriptome sequencing (RNA-Seq) technology was applied to the host transcript profiles of C6/36 cells infected with DENV2. Then, bioinformatics analysis was used to identify significant differentially expressed genes and the associated biological processes. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed to verify the sequencing data. A total of 1239 DEGs were found by transcriptional analysis of Aedes albopictus C6/36 cells that were infected and uninfected with dengue virus, among which 1133 were upregulated and 106 were downregulated. Further bioinformatics analysis showed that the upregulated DEGs were significantly enriched in signaling pathways such as the MAPK, Hippo, FoxO, Wnt, mTOR, and Notch; metabolic pathways and cellular physiological processes such as autophagy, endocytosis, and apoptosis. Downregulated DEGs were mainly enriched in DNA replication, pyrimidine metabolism, and repair pathways, including BER, NER, and MMR. The qRT-PCR results showed that the concordance between the RNA-Seq and RT-qPCR data was very high (92.3%). The results of this study provide more information about DENV2 infection of C6/36 cells at the transcriptome level, laying a foundation for further research on mosquito vector–virus interactions. These data provide candidate antiviral genes that can be used for further functional verification in the future.

scholarly journals Transcriptomic and ChIP-seq Integrative Analysis Reveals Important Roles of Epigenetically Regulated lncRNAs in Placental Development in Meishan Pigs

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 397
Author(s):  
Dadong Deng ◽  
Xihong Tan ◽  
Kun Han ◽  
Ruimin Ren ◽  
Jianhua Cao ◽  
...  
Keyword(s):  
Differentially Expressed ◽  
Rna Seq ◽  
Sequencing Data ◽  
Placental Development ◽  
Cytoskeleton Organization ◽  
New Class ◽  
Chromatin Immunoprecipitation Sequencing ◽  
Non Coding Rnas ◽  
Two Stages ◽  
Regulatory Functions

The development of the placental fold, which increases the maternal–fetal interacting surface area, is of primary importance for the growth of the fetus throughout the whole pregnancy. However, the mechanisms involved remain to be fully elucidated. Increasing evidence has revealed that long non-coding RNAs (lncRNAs) are a new class of RNAs with regulatory functions and could be epigenetically regulated by histone modifications. In this study, 141 lncRNAs (including 73 up-regulated and 68 down-regulated lncRNAs) were identified to be differentially expressed in the placentas of pigs during the establishment and expanding stages of placental fold development. The differentially expressed lncRNAs and genes (DElncRNA-DEgene) co-expression network analysis revealed that these differentially expressed lncRNAs (DElncRNAs) were mainly enriched in pathways of cell adhesion, cytoskeleton organization, epithelial cell differentiation and angiogenesis, indicating that the DElncRNAs are related to the major events that occur during placental fold development. In addition, we integrated the RNA-seq (RNA sequencing) data with the ChIP-seq (chromatin immunoprecipitation sequencing) data of H3K4me3/H3K27ac produced from the placental samples of pigs from the two stages (gestational days 50 and 95). The analysis revealed that the changes in H3K4me3 and/or H3K27ac levels were significantly associated with the changes in the expression levels of 37 DElncRNAs. Furthermore, several H3K4me3/H3K27ac-lncRNAs were characterized to be significantly correlated with genes functionally related to placental development. Thus, this study provides new insights into understanding the mechanisms for the placental development of pigs.

scholarly journals The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Xueyi Dong ◽  
Luyi Tian ◽  
Quentin Gouil ◽  
Hasaru Kariyawasam ◽  
Shian Su ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Transcriptomic Analysis ◽  
Statistical Testing ◽  
Rna Seq ◽  
Sequencing Data ◽  
Short Read ◽  
Sequencing Platform ◽  
Long Read

Abstract Application of Oxford Nanopore Technologies’ long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs (‘sequins’) as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator Smchd1 was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.

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